Data compression implementation

ABSTRACT

There is disclosed herein examples of systems and methods for compressing a signal. Samples of the signal can be segmented and the samples within each of the segments can be averaged to produce a value that can represent the samples within the segment. The number of samples to average in each segment may be determined based on an error threshold, such that the number of samples being averaged can be maximized to produce less data to be transmitted while maintaining the representation of the samples within the error threshold. In some embodiments, a signal can be separated into a timing reference, a representative periodic function, and a highly compressible error signal. The error signal can be utilized for reproducing a representation of the signal.

RELATED APPLICATIONS

The present disclosure claims priority to U.S. provisional applicationNo. 62/804,718 entitled “DATA COMPRESSION IMPLEMENTATION” and filed Feb.12, 2019, the disclosure of which is incorporated by reference in itsentirety.

FIELD OF THE DISCLOSURE

This disclosure relates in general to the field of data compression, andmore particularly, though not exclusively, to systems and methods ofdata compression for wearable monitors.

BACKGROUND

With the advent of mobile devices and the limited supply of power fromthe battery of the mobile devices, providing power-efficient wirelesscommunication of data has been prevented as a challenge. In particular,transmission of data via a transmitter of a mobile device can drain thepower from the battery. Some legacy approaches to reduce the power drainof wireless communication include generating compressed signalsrepresenting an original signal, where the compressed signals could betransmitted while drawing less power than transmission of the originalsignal. However, the legacy approaches struggle with providinginadequate representations of the original signal due to the compressionand/or lacking optimization of the compression leading to lack ofoptimization of the power draw.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is best understood from the following detaileddescription when read with the accompanying figures. It is emphasizedthat, in accordance with the standard practice in the industry, variousfeatures are not necessarily drawn to scale, and are used forillustration purposes only. Where a scale is shown, explicitly orimplicitly, it provides only one illustrative example. In otherembodiments, the dimensions of the various features may be arbitrarilyincreased or reduced for clarity of discussion.

FIG. 1 illustrates an example data encode procedure, according toembodiments of the present disclosure.

FIG. 2 illustrates an example decimation determination procedure,according to embodiments of the present disclosure.

FIG. 3A illustrates a graphical representation of a first approximationof the example decimation determination procedure of FIG. 2, accordingto embodiments of the present disclosure.

FIG. 3B illustrates a graphical representation of a second approximationof the example decimation determination procedure of FIG. 2, accordingto embodiments of the present disclosure.

FIG. 3C illustrates a graphical representation of a third approximationof the example decimation determination procedure of FIG. 2, accordingto embodiments of the present disclosure.

FIG. 3D illustrates a graphical representation of an error determinationof the first approximation of the example decimation determinationprocedure of FIG. 2, according to embodiments of the present disclosure.

FIG. 3E illustrates a graphical representation of an error determinationof the second approximation of the example decimation determinationprocedure of FIG. 2, according to embodiments of the present disclosure.

FIG. 3F illustrates a graphical representation of an error determinationof the third approximation of the example decimation determinationprocedure of FIG. 2, according to embodiments of the present disclosure.

FIG. 4 illustrates an example data decode procedure, according toembodiments of the present disclosure.

FIG. 5 illustrates an example segment determination procedure, accordingto embodiments of the present disclosure.

FIG. 6 illustrates a graphical representation of the example segmentdetermination procedure of FIG. 5, according to embodiments of thepresent disclosure.

FIG. 7 illustrates an example training procedure, according toembodiments of the present disclosure.

FIG. 8 illustrates an example compression procedure, according toembodiments of the present disclosure.

FIG. 9 illustrates example circuitry of a device, according toembodiments of the present disclosure.

FIG. 10 illustrates an example arrangement, according to embodiments ofthe present disclosure.

EMBODIMENTS OF THE DISCLOSURE

The following disclosure provides many different embodiments, orexamples, for implementing different features of the present disclosure.Specific examples of components and arrangements are described below tosimplify the present disclosure. These are, of course, merely examplesand are not intended to be limiting. Further, the present disclosure mayrepeat reference numerals and/or letters in the various examples, or insome cases across different figures. This repetition is for the purposeof simplicity and clarity and does not in itself dictate a specificrelationship between the various embodiments and/or configurationsdiscussed. Different embodiments may have different advantages, and noparticular advantage is necessarily required of any embodiment.

FIG. 1 illustrates an example data encode procedure 100, according toembodiments of the present disclosure. The data encode procedure 100 maybe performed by a microprocessor or other circuitry. In someembodiments, computer-readable media may store instructions that, whenexecuted by the microprocessor or another device, cause themicroprocessor or the device to perform the data encode procedure 100.Further, the data encode procedure 100 may be performed by a mobile datacollection device (such as an electrocardiogram (ECG) monitor) thatperforms the data encode procedure 100 on data captured by the datacollection device prior to transmitting (either wirelessly or wiredly)encoded data representing the captured data to a remote device.

In stage 102 of the data encode procedure 100, data may be received bythe entity performing the data encode procedure 100. The data maycomprise an analog signal, a digital signal, samples captured from ananalog or digital signal, or some combination thereof. For example, thedata may comprise an analog signal detected by an ECG sensor.

In stage 104, the entity may scale and/or truncate the data. Forexample, the entity may discard bits below a predetermined noise level.Discarding of the bits may minimize the number of delta codes applied instage 114. In some embodiments where an analog signal is received instage 102, stage 104 may further include sampling the analog signal toproduce a plurality of samples representing the analog signal. Forexample, the entity may sample the analog signal at a set frequencywhere a sample value of the analog signal is produced each time theentity samples the analog signal.

In stage 106, the entity may determine a decimation map for the data.Determination of the decimation map is described further in relation toFIG. 2.

In stage 108, the entity may filter the decimation map. In particular,the entity may identify low energy outlier segments within the data andabsorb the outlier segments into a neighboring high-energy cluster. Insome embodiments, stage 108 may be omitted.

In stage 110, a decimation map may be produced by the entity. Thedecimation map may be produced based on the determination of thedecimation map and/or the filtering of the decimation map performed instage 106 and stage 108, respectively. In some embodiments, thedecimation map may be compressed for transmission. For example, theentity may apply run-length encoding to the decimation map to compressthe decimation map. The entity may transmit, or provide for transmissionof, the decimation map to a remote device, where the remote device mayutilize the decimation map to produce a representation of the datareceived in stage 102. The decimation map transmitted to the remotedevice may be compressed in some embodiments and may not be compressedin other embodiments.

In stage 112, the entity may decimate the data. In particular, theentity may decimate the data in accordance with the decimation mapproduced in stage 110. The decimation of the data is described furtherin FIG. 2.

In stage 114, the entity may apply delta coding to the decimated data.For example, the entity may take a first difference of the data toreduce a number of codes. In some embodiments, stage 114 may be omitted.

In stage 116, a fixed Huffman dictionary may be received for encoding ofthe decimated data by the entity. In some embodiments, stage 116 may beomitted.

In stage 118, the entity may apply Huffman coding to the decimated data.In some embodiments, the entity may apply the Huffman coding to thedecimated signal after delta coding has been applied to the decimateddata. For example, the entity may encode the most common values withinthe decimated data with shorter codes.

In stage 120, the entity may produce a Huffman dictionary. The Huffmandictionary may be based on the decimated data and may comprise adata-driven Huffman dictionary. In some embodiments, the producedHuffman dictionary may be based from the fixed Huffman dictionaryreceived in stage 116.

In stage 122, the entity may produce encoded data that represents thedata received in stage 102. The encoded data may have been produced viathe decimation, delta coding, and/or Huffman coding performed in stage112, stage 114, and stage 118, respectively.

FIG. 2 illustrates an example decimation determination procedure 200,according to embodiments of the present disclosure. The decimationdetermination procedure 200 may be performed as part of stage 106, stage108, stage 110, and/or stage 112 of FIG. 1. In particular, thedecimation determination procedure 200 may be utilized for determinationof the decimation map, production of the decimation map, and/orperforming decimation of the data. FIG. 3A through FIG. 3F illustrategraphical representations of the example decimation determinationprocedure 200 of FIG. 2, according to embodiments of the presentdisclosure.

In stage 202, an entity may identify a data segment to be decimated. Thedata segment may include a segment of data received by the entity to bedecimated or an entirety of data received by the entity to be decimated.For example, the data segment may include a segment of the data receivedby the entity in stage 102 (FIG. 1) or may include an entirety of thedata received by the entity in stage 102. The data samples included inthe data segment may be identified by the entity based on a certainfeature of the data samples, such as the data samples exceeding acertain value, a sign of the value of the data samples, a certainportion of a signal represented by the data samples, or some combinationthereof.

In stage 204, the entity may generate a first order approximation of thedata samples within the data segment. Generating the first orderapproximation may include determining a number of data samples to beaveraged to produce the approximation or may include determining anumber of approximation segments to be made of the data samples. Thenumber of data samples or the number of approximation segments for thefirst order approximation may be selected to produce a minimized numberof approximation segments. For example, the number of data samples orthe number of approximation segments for the first order approximationmay be selected to produce one approximation segment in someembodiments. For each sample within the data segment, the entity mayproduce a corresponding sample in the approximation segments.

FIG. 3A illustrates a graphical representation of a first approximationof the example decimation determination procedure of FIG. 2, accordingto embodiments of the present disclosure. In particular, chart 302illustrates an example representation of a first order approximation ofa data segment 304. The data segment 304 may include eight samples. Insome embodiments, the data segment 304 may represent a portion of an ECGsignal. The first order approximation may include a single approximationsegment generated from the eight samples. The entity may determine amean of the eight samples and set the value of the eight correspondingsamples of the approximation segment 306 to the mean of the eightsamples.

In stage 206, the entity may determine whether an error of the firstorder approximation is greater than an error threshold. In particular,the entity may compare the samples of the first order approximation withthe corresponding samples of the data segment to determine whether thedifference between each of the samples of the first order approximationand the samples of the data segment exceed the error threshold. Theerror threshold may be a percentage, a magnitude, or some combinationthereof. The error threshold may be the same for each data segmentreceived by the entity or may vary among different data segmentsreceived by the entity. Further, the error threshold may bepredetermined, may be determined based on the data segment, or may beprogrammable by a user of the entity. For example, the error thresholdfor each data segment may be determined based on the feature utilizedfor determining to include the samples in the data segment. The entitymay utilize the maximum absolute error value, the root-mean-squarederror value, or any other suitable metric, for determining whether theerror is greater than the error threshold. If the error of the firstorder approximation is less than the error threshold, the decimationdetermination procedure 200 may proceed to stage 208. If the error ofthe first order approximation is greater than the error threshold, thedecimation determination procedure 200 may proceed to stage 210.

FIG. 3D illustrates a graphical representation of an error determinationof the first approximation of the example decimation determinationprocedure of FIG. 2, according to embodiments of the present disclosure.In particular, chart 308 illustrates an example representation of thedetermination of whether the maximum absolute value error of the firstorder approximation is greater than the error threshold. In theillustrated embodiment, the error threshold is set to a value of 5. Theentity may produce an error threshold envelope 310 from −5 to 5. Theentity may further produce an error amount line 312 by determining thedifference between each of the samples of the approximation segment 306and the corresponding samples of the data segment 304. The entity maythen determine whether the error amount line 312 is within the errorthreshold envelope 310, thereby determining whether the error of thefirst order approximation is greater than the error threshold.

In stage 208, the entity may determine that the first orderapproximation is to be utilized based on the error of the first orderapproximation being less than the error threshold. For example, theentity may utilize the first order approximation for stage 106, stage108, stage 110, and stage 112 of the data encode procedure 100 (FIG. 1).The decimation determination procedure 200 may be exited at stage 208.

In stage 210, the entity may generate a second order approximation ofthe data samples within the data segment. Generating the second orderapproximation may include determining a number of data samples to beaveraged to produce the approximation or may include determining anumber of approximation segments to be made of the data samples. Thenumber of data samples or the number of approximation segments for thesecond order approximation may be selected to produce more approximationsegments than the first order approximation. For example, the number ofdata samples or the number of approximation segments for the secondorder approximation may be selected to produce two approximationsegments in some embodiments. For each sample within the data segment,the entity may produce a corresponding sample in the approximationsegments.

FIG. 3B illustrates a graphical representation of a second approximationof the example decimation determination procedure of FIG. 2, accordingto embodiments of the present disclosure. In particular, chart 314illustrates an example representation of a second order approximation ofthe data segment 304. The second order approximation may include twoapproximation segments. In particular, the second order approximationmay include a first approximation segment 318 generated from the firstfour samples of the data segment 304 and a second approximation segment320 generated from the last four samples of the data segment 304. Theentity may determine a mean of the first four samples and set the valueof the four corresponding samples of the first approximation segment 318to the mean of the first four samples. Further, the entity may determinea mean of the last four samples and set the value of the fourcorresponding samples of the second approximation segment 320 to themean of the last four samples.

In stage 212, the entity may determine whether an error of the secondorder approximation is greater than the error threshold. In particular,the entity may compare the samples of the second order approximationwith the corresponding samples of the data segment to determine whetherthe difference between each of the samples of the second orderapproximation and the samples of the data segment exceed the errorthreshold. The entity may utilize the maximum absolute error value, theroot-mean-squared error value, or any other suitable metric, fordetermining whether the error is greater than the error threshold. Ifthe error of the second order approximation is less than the errorthreshold, the decimation determination procedure 200 may proceed tostage 214. If the error of the second order approximation is greaterthan the error threshold, the decimation determination procedure 200 mayproceed to stage 216.

FIG. 3E illustrates a graphical representation of an error determinationof the second approximation of the example decimation determinationprocedure of FIG. 2, according to embodiments of the present disclosure.In particular, chart 322 illustrates an example representation of thedetermination of whether the error of the second order approximation isgreater than the error threshold. In the illustrated embodiment, theerror threshold is set to a value of 5. The entity may produce an errorthreshold envelope 324 from −5 to 5. The entity may further produce anerror amount line 326 by determining the difference between each of thesamples of the first approximation segment 318 and the correspondingsamples of the data segment 304, and between each of the samples of thesecond approximation segment 320 and the corresponding samples of thedata segment 304. The entity may then determine whether the error amountline 326 is within the error threshold envelope 324, thereby determiningwhether the error of the first order approximation is greater than theerror threshold.

In stage 214, the entity may determine that the second orderapproximation is to be utilized based on the error of the second orderapproximation being less than the error threshold. For example, theentity may utilize the second order approximation for stage 106, stage108, stage 110, and stage 112 of the data encode procedure 100 (FIG. 1).The decimation determination procedure 200 may be exited at stage 214.

In stage 216, the entity may generate a third order approximation of thedata samples within the data segment. Generating the third orderapproximation may include determining a number of data samples to beaveraged to produce the approximation or may include determining anumber of approximation segments to be made of the data samples. Thenumber of data samples or the number of approximation segments for thethird order approximation may be selected to produce more approximationsegments than the second order approximation. For example, the number ofdata samples or the number of approximation segments for the third orderapproximation may be selected to produce four approximation segments insome embodiments. For each sample within the data segment, the entitymay produce a corresponding sample in the approximation segments.

FIG. 3C illustrates a graphical representation of a third approximationof the example decimation determination procedure of FIG. 2, accordingto embodiments of the present disclosure. In particular, chart 328illustrates an example representation of a third order approximation ofthe data segment 304. The third order approximation may include fourapproximation segments. In particular, the third order approximation mayinclude a first approximation segment 330 generated from the first twosamples of the data segment 304, a second approximation segment 332generated from the next two samples of the data segment 304, a thirdapproximation segment 334 generated from the next two samples of thedata segment 304, and a fourth approximation segment 336 generated fromthe last two samples of the data segment 304. The entity may determine amean of the first two samples and set the value of the two correspondingsamples of the first approximation segment 330 to the mean of the firsttwo samples. The entity may determine a mean of the next two samples andset the value of the two corresponding samples of the secondapproximation segment 332 to the mean of the two samples. The entity maydetermine a mean of the next two samples and set the value of the twocorresponding samples of the third approximation segment 334 to the meanof the two samples. The entity may determine a mean of the last twosamples and set the value of the two corresponding samples of the fourthapproximation segment 336 to the mean of the two samples.

In stage 218, the entity may determine whether an error of the thirdorder approximation is greater than the error threshold. In particular,the entity may compare the samples of the third order approximation withthe corresponding samples of the data segment to determine whether thedifference between each of the samples of the third order approximationand the samples of the data segment exceed the error threshold. Theentity may utilize the maximum absolute error value, theroot-mean-squared error value, or any other suitable metric, fordetermining whether the error is greater than the error threshold. Ifthe error of the third order approximation is less than the errorthreshold, the decimation determination procedure 200 may proceed tostage 220. If the error of the third order approximation is greater thanthe error threshold, the decimation determination procedure 200 mayproceed to stage 222.

FIG. 3F illustrates a graphical representation of an error determinationof the third approximation of the example decimation determinationprocedure of FIG. 2, according to embodiments of the present disclosure.Chart 338 illustrates an example representation of the determination ofwhether the error of the third order approximation is greater than theerror threshold. In the illustrated embodiment, the error threshold isset to a value of 5. The entity may produce an error threshold envelope340 from −5 to 5. The entity may further produce an error amount line342 by determining the difference between each of the samples of thefirst approximation segment 330 and the corresponding samples of thedata segment 304, between each of the samples of the secondapproximation segment 332 and the corresponding samples of the datasegment 304, between each of the samples of the third approximationsegment 334 and the corresponding samples of the data segment 304, andbetween each of the samples of the fourth approximation segment 336 andthe corresponding samples of the data segment 304. The entity may thendetermine whether the error amount line 342 is within the errorthreshold envelope 340, thereby determining whether the error of thethird order approximation is greater than the error threshold.

In stage 220, the entity may determine that the third orderapproximation is to be utilized based on the error of the third orderapproximation being less than the error threshold. For example, theentity may utilize the third order approximation for stage 106, stage108, stage 110, and stage 112 of the data encode procedure 100 (FIG. 1).The decimation determination procedure 200 may be exited at stage 220.

In stage 222, the entity may have determined that the approximations maynot be performed while the error threshold is satisfied. In this case,the entity may utilize all the data samples of the data segment. Forexample, the entity may utilize all the data samples for stage 106,stage 108, and stage 112 of the data encode procedure 100. Thedecimation determination procedure 200 may be exited at stage 222.

While three orders of approximation are described in relation to thedecimation determination procedure 200, it is to be understood that thedecimation determination procedure 200 may include greater or fewerorders of approximation in other embodiments. Further, the amount ofsegments for each of the orders of approximation are examples and theamount of segments may differ in other embodiments.

The decimation determination procedure 200 may be repeated for each datasegment to be decimated, where the error threshold may be different fordifferent data segments. In some embodiments, the entity may limit achange in the order of approximation for decimation between consecutivesegments of the data received by the entity. For example, the datareceived by the entity may comprise a signal, where the differencebetween the order of approximation utilized for decimation ofconsecutive portions of the signal may be limited to be below athreshold difference. As an example, if a first segment of the data isdetermined to be decimated using a second order approximation and thethreshold difference is one order, the order of approximation fordecimation of the subsequent segment of the data may be limited towithin one order of the first segment, which can be a first orderapproximation, a second order approximation, or a third orderapproximation being determined for decimation of the subsequent segment.Limiting the change in the order of approximation for decimation betweenconsecutive segments of data may reduce artifacts in a reconstructedsignal.

One or more decimation maps may be produced based on the decimationdetermination procedure 200. The decimation maps may indicate the orderof approximation that has been determined for decimating the samples andthe decimation that has been performed on the samples. The decimationmaps may be utilized to create a representation of the signal based onthe results of the decimation of the samples.

FIG. 4 illustrates an example data decode procedure 400, according toembodiments of the present disclosure. The data decode procedure 400 maybe performed by a device remote from the entity that performs the dataencode procedure 100 (FIG. 1). For example, an ECG monitor may performthe data encode procedure 100 and a data server separate from the ECGmonitor may perform the data decode procedure 400 in some embodiments.

In stage 402, the device may receive encoded data. In particular, thedevice may receive the encoded data produced by the entity in stage 122(FIG. 1). The device may receive the encoded data wirelessly or wiredlyfrom the entity.

In stage 404, the device may receive or access a Huffman dictionary. Forexample, the Huffman dictionary may be a fixed Huffman dictionary (suchas the fixed Huffman dictionary referenced in stage 116 (FIG. 1)), andthe device may access the fixed Huffman dictionary from memory of thedevice or receive the fixed Huffman dictionary from the entity in someembodiments. In other embodiments, the entity may produce a Huffmandictionary (such as the Huffman dictionary referenced in stage 120 (FIG.1)) and provide the Huffman dictionary to the device.

In stage 406, the device may apply Huffman decoding to the encoded data.In particular, the device may utilize the Huffman dictionary received oraccessed in stage 404 to apply Huffman decoding to the encoded data. Insome embodiments, stage 404 and stage 406 may be omitted. For example,when Huffman coding was not utilized to generate the encoded datareceived in stage 402, stage 404 and stage 406 may be omitted.

In stage 408, the device may apply delta decoding to the encoded data.In some embodiments, stage 408 may be omitted. For example, when deltacoding was not utilized to generate the encoded data received in stage402, stage 408 may be omitted.

In stage 410, the device may receive a decimation map. The decimationmap may be a decimation map produced by the entity while generating theencoded data. For example, the device may receive the decimation mapproduced in stage 110 (FIG. 1).

In stage 412, the device interpolates according to the decimation map.For example, device may utilize samples of the encoded data received instage 402 and the decimation map to create reconstructed data.

In stage 414, the device produces reconstructed data. The reconstructeddata may be based on the interpolation according to the decimation mapperformed in stage 412. The reconstructed data may include a signal,such as an analog signal or a digital signal.

FIG. 5 illustrates an example segment determination procedure 500,according to embodiments of the present disclosure. The segmentdetermination procedure 500 may be performed by the entity that performsthe data encode procedure 100. FIG. 6 illustrates a graphicalrepresentation of the example segment determination procedure 500 ofFIG. 5, according to embodiments of the present disclosure.

In stage 502, the entity may receive data. The data may include a signal(such as an analog or a digital signal) or samples of a signal. In someembodiments, the signal may be an ECG signal. FIG. 6 illustrates asignal 602 that may be received by the entity.

In stage 504, the entity may identify one or more occurrences ofsegmentation features. The segmentation features may comprise anycharacteristic of the received data that may be utilized by the entityfor determining segments of the data. For example, the segmentationfeatures may include certain voltage ranges that indicate which segmenta portion of the data is to be assigned, a certain voltage thatindicates a new segment of the data is to be assigned, a certain timerange that indicates which segment a portion of the data is to beassigned, a certain time that indicates a new segment of the data is tobe assigned, or some combination thereof. In some embodiments, thesegmentation features may indicate that portions of the data that exceeda certain magnitude are to be assigned to certain segments, whileportions of the data below the certain magnitude are to be assigned toother segments. Line 604 of FIG. 6 illustrates a magnitude segmentationfeature, where portions of the signal 602 with magnitudes greater thanthe line 604 are to be assigned to certain portions and portions of thesignal 602 with magnitudes less than the line 604 are to be assigned toother portions.

In stage 506, the entity assigns segments to the data. In particular,the entity assigns segments to the data based on the segmentationfeatures. FIG. 6 illustrates results of assigning the segments based onthe magnitude segmentation feature indicated by the line 604. The entitymay assign a first segment 606 to a first portion of the signal 602based on the magnitude of the first portion being less than themagnitude indicated by the line 604. The entity may assign a secondsegment 608 to a second portion of the signal 602 based on the magnitudeof the second portion being greater than the magnitude indicated by theline 604. Further, the entity may assign a third segment 610 to a thirdportion of the signal 602 based on the magnitude of the third portionbeing less than the magnitude indicated by the line 604 and beingseparate from the first portion.

In stage 508, the entity may provide the segments. Providing thesegments may comprise sequentially providing each of the segments orindications of the assignments of the segments. The segments may beutilized by the data encode procedure 100 for allowing the encoding tobe performed on each segment to be determined individually. For example,the entity may determine that a certain decimation map may be utilizedfor one of the segments and a different decimation map may be utilizedfor another of the segments. Further, each of the segments may beindividually provided in stage 102 for performance of the data encodeprocedure 100, or all segments may be provided in stage 102 and then maybe processed individually.

In some instances, a signal of interest, such as an ECG, may be largelyperiodic in the sense that most of the useful information contained inthe signal can be expressed by a model comprising first, a detailedpattern representing a single period of the signal, and second, a timingreference that indicates the rate at which that pattern repeats. In suchcases, the residual signal representing the difference between the modeland the actual signal may be utilized to reconstruct the completesignal. However, this residual signal may contain few features ofinterest and thus, it may be highly compressible. Accordingly, acompression approach may comprise of a training procedure 700 followedby a compression procedure 800. FIG. 7 illustrates an example trainingprocedure 700, according to embodiments of the present disclosure. Insome embodiments, the entity may perform the training procedure 700 totake advantage of signals that are periodic. The training procedure 700may be performed by the same entity that performs the data encodeprocedure 100 (FIG. 1).

In stage 702, the entity may receive one or more lead measurements froma sensor. The lead measurement may comprise a signal, and, in someembodiments, may comprise an ECG signal. The sensor may comprise one ormore electrodes to capture the signal. In some instances, the electrodesmay be placed on the skin of a subject and capture the signal from thesubject.

In stage 704, the entity may record the signal received via themeasurement as one or more training sequences. A training sequence mayinclude one or more repetitions of the signal, which is periodic. Thetraining sequences may be stored in memory.

In stage 706, the entity may find peaks of each the repetitions of thesignal within the training sequences. For example, the trainingsequences may be retrieved from memory and peaks may be found in thetraining sequences. The peaks may be all the peaks within therepetitions, a certain portion of the peaks within the repetitions, apeak with the greatest magnitude within each of the repetitions, a peakwith the greatest positive magnitude within each of the repetitions, apeak with the greatest negative magnitude within each of therepetitions, or some combination thereof. In some embodiments, the peakmay be an R peak of an ECG signal, where the R peak may be due todepolarization of the ventricular Myocardium.

In stage 708, the entity may generate a timing reference. In someembodiments, the timing reference may be a 1-bit timing reference thatmay be utilized to indicate the timing of the peaks identified in stage706.

In stage 710, the entity may construct a linear regression model of thetraining sequences. The entity may construct the linear regression modelbased on the lead measurement, the identified peaks, and the timingreference.

In stage 712, the entity may provide the linear regression model as alead model. The lead model may be utilized to reconstruct a signal. Forexample, the lead model may comprise a base for a signal, where a timingreference (such as the timing reference produced in stage 806 (FIG. 8))when used with the lead model may produce a signal.

FIG. 8 illustrates an example compression procedure 800, according toembodiments of the present disclosure. The compression procedure 800 maybe performed by the same entity that performs the data encode procedure100 (FIG. 1) and the training procedure 700 (FIG. 7).

In stage 802, the entity may receive one or more lead measurements froma sensor. The lead measurement may comprise a signal, and, in someembodiments, may comprise an ECG signal. The sensor may comprise one ormore electrodes to capture the signal. In some instances, the electrodesmay be placed on the skin of a subject and capture the signal from thesubject. The lead measurements may be stored in a memory.

In stage 804, the entity may find peaks of the lead measurements. Forexample, the lead measurements may be retrieved from memory and thepeaks may be found in the lead measurements. The peaks may be all thepeaks within the lead measurement, a certain portion of the peaks withinthe lead measurement, a peak with the greatest magnitude within the leadmeasurement, a peak with the greatest positive magnitude within the leadmeasurement, a peak with the greatest negative magnitude within the leadmeasurement, or some combination thereof. In some embodiments, the peakmay be an R peak of an ECG signal, where the R peak may be due todepolarization of the ventricular Myocardium.

In stage 806, the entity may produce a timing reference. In someembodiments, the timing reference may be a 1-bit timing reference.

In stage 808, the entity may receive and/or access a lead model. Thelead model may be the lead model provided by the training procedure 700in stage 712. The lead model may comprise a base for a signal, where atiming reference (such as the timing reference produced in stage 806)when used with the lead model may produce a signal.

In stage 810, the entity may perform reconstruction of an estimate ofthe lead measurement. The entity may reconstruct the estimate based onthe lead model and the timing reference. The estimate of the leadmeasurement may comprise an estimate of the signal of the leadmeasurement. The reconstruction with the lead model and the timingreference may produce a signal that estimates the signal of the leadmeasurement from stage 802.

In stage 812, the entity may produce a lead estimate. The lead estimatemay be produced by the reconstruction performed in stage 810. The leadmodel may comprise an estimated signal that estimates the signal of thelead measurement from stage 802.

In stage 814, the entity may sum the lead measurement received in stage802 and the lead estimate produced in stage 812. In particular, theentity may sum the positive value of the lead measurement received instage 802 with the negative value of the lead estimate to determine adifference between the lead measurement and the lead estimate.

In stage 816, the entity may compress the results of the summationperformed in stage 814 using any suitable compression approach, such asthe data encode procedure 100 (FIG. 1). The results of the compressionmay comprise an error between the lead measurement and the leadestimate.

In stage 818, the entity may produce a lead error. The lead error may bea compressed representation of the difference between the leadmeasurement received in stage 802 and the lead estimate produced instage 812. The lead error may be utilized to produce a signal thatrepresents the signal of the lead measurement. For example, the leaderror may be utilized with a lead model (such as the lead model producedin stage 712 (FIG. 7)) to produce a signal that estimates the signal ofthe lead measurement. The lead error may be highly compressible based onthe information represented by the lead error.

FIG. 9 illustrates example circuitry 900 of a device, according toembodiments of the present disclosure. The device may comprise an ECGmonitor, where the circuitry 900 may comprise a portion of the circuitryof the ECG monitor. The circuitry 900, or some portion thereof, mayperform one or more of the procedures described herein, including thedata encode procedure 100 (FIG. 1), the decimation determinationprocedure 200 (FIG. 2), the segment determination procedure 500 (FIG.5), training procedure 700 (FIG. 7), and/or the compression procedure800 (FIG. 8). In some embodiments, a microcontroller 902 of thecircuitry 900 may perform the procedures described herein.

The circuitry 900 may include an ECG lead input 904. The ECG lead input904 may be coupled to an ECG lead that is to be applied to a subject andproduce a signal that represents the electrical activity of a heart ofthe subject. The ECG lead input 904 may receive the signal produced bythe ECG lead.

The circuitry 900 may further include an analog-to-digital converter(ADC) 906. The ADC 906 may be coupled to the ECG lead input 904 and mayreceive the signal from the ECG lead input 904, where the signal isreceived in analog format. The ADC 906 may produce a digital formatrepresentation of the of the signal. For example, the ADC 906 may samplethe signal in analog format and may produce a plurality of samples ofthe signal that may be utilized in the data encode procedure 100, thedecimation determination procedure 200, the segment determinationprocedure 500, the training procedure 700, and/or the compressionprocedure 800. In other embodiments, the ADC 906 may be omitted from thecircuitry 900 and the microcontroller 902 may perform theanalog-to-digital conversion.

The circuitry 900 may further include the microcontroller 902. Themicrocontroller 902 may comprise one or more processors, such as one ormore microprocessors. The microcontroller 902 may be coupled to the ADC906 and/or the ECG lead input 904, and may receive the output from theADC 906 or the output of the ECG lead input 904. The microcontroller 902may perform one or more of the operations described herein with the datareceived from the ADC 906 or the ECG lead input 904. In someembodiments, the entity described throughout this disclosure may referto the microcontroller 902.

The circuitry 900 may further include one or more memory devices 908.The memory devices 908 may be coupled to the microcontroller 902. Thememory devices 908 may comprise computer-readable memory devices thatmay store instructions that, when executed by the microcontroller 902,cause the microcontroller 902 to perform one or more of the operationsdescribed herein. Further, the microcontroller 902 may utilize thememory devices 908 to store data, such as the results of the operationperformed by the microcontroller 902 on the data received from the ADC906 or the ECG lead input 904.

The circuitry 900 may further include a transceiver 910. In someembodiments, the transceiver 910 may comprise low energy Bluetoothcircuitry and an antenna. In other embodiments, the transceiver 910 maycomprise any components that facilitate wireless and/or wired datalinks, such as universal serial bus (USB) circuitry. The microcontroller902 may utilize the transceiver 910 to transmit data to a remote device.For example, the microcontroller 902 may provide data to the transceiver910, where the transceiver 910 may convert the data for transmission.

FIG. 10 illustrates an example arrangement 1000, according toembodiments of the present disclosure. The arrangement 1000 mayimplement the entity described herein.

The arrangement 1000 may include a device 1002. The device 1002 may be amonitoring device in some embodiments. For example, the device 1002 maycomprise a monitoring device, such as an ECG monitor. The device 1002may include a microcontroller 1004, which may perform and/or control oneor more of the operations performed by the device 1002. In someembodiments, the device 1002 may implement the circuitry 900 (FIG. 9),where the microcontroller 1004 may comprise the microcontroller 902(FIG. 9) of the circuitry 900. The entity described throughout thisdisclosure may refer to the device 1002 in some embodiments or to themicrocontroller 1004 in other embodiments. The device 1002, or someportion thereof, may perform one or more of the operations of the entitydescribed herein, including the data encode procedure 100 (FIG. 1), thedecimation determination procedure 200 (FIG. 2), the segmentdetermination procedure 500 (FIG. 5), the training procedure 700 (FIG.7), and/or the compression procedure 800 (FIG. 8).

The arrangement 1000 may further include a network 1006. The network1006 may be coupled to multiple devices and may provide for wirelesscommunication among the devices. For example, the network 1006 may becoupled to the device 1002 and may provide for wireless communicationwith the device 1002. In some embodiments, the network 1006 may comprisea Bluetooth network.

The arrangement 1000 may further include a remote device 1008. Theremote device 1008 may comprise a computer device that is separate fromthe device 1002. The remote device 1008 may be coupled to the network1006 and may exchange data with the device 1002 via the network 1006. Insome embodiments, the remote device 1008 may perform the data decodeprocedure 400. Further, the remote device 1008 may comprise anintermediary device that facilitates communication between the device1002 and another remote device (such as a server) via the network 1006or another network. In other embodiments, the remote device 1008 maycomprise a server that can exchange data with the device 1002 andanalyze the data received from the device.

EXAMPLE IMPLEMENTATIONS

The following examples are provided by way of illustration.

Example 1 may include a device, comprising a memory device to storesamples of a signal, and a microcontroller coupled to the memory device,the microcontroller to assign a first portion of the samples to a firstsegment based on a segmentation feature of the first portion of thesamples, assign a second portion of the samples to a second segmentbased on a segmentation feature of the second portion of the samples,perform decimation of the first segment, wherein the decimation of thefirst segment utilizes a first segment order approximation for thedecimation of the first segment, and perform decimation of the secondsegment, wherein the decimation of the second segment utilizes a secondsegment order approximation for the decimation of the second segment.

Example 2 may include the device of example 1, wherein the segmentationfeature of the first portion of the samples comprises values of thefirst portion of the samples exceeding a certain magnitude, and whereinthe segmentation feature of the second portion of the samples comprisesvalues of the second portion of the samples being below the certainmagnitude.

Example 3 may include the device of example 1, wherein the first segmentorder approximation is determined based on an average of the firstportion of the samples and a first error threshold for the firstsegment, and wherein the second segment order approximation isdetermined based on an average of the second portion of the samples anda second error threshold for the second segment.

Example 4 may include the device of example 3, wherein the first errorthreshold is determined based on the segmentation feature of the firstportion of the samples, and wherein the second error threshold isdetermined based on the segmentation feature of the second portion ofthe samples.

Example 5 may include the device of example 4, wherein the first errorthreshold and the second error threshold are different.

Example 6 may include the device of example 1, wherein the first segmentorder approximation is of a different order from the second segmentorder approximation.

Example 7 may include the device of example 1, wherein themicrocontroller is further to generate one or more approximations of thefirst segment, compare one or more values of the one or moreapproximations of the first segment with values of the first portion ofthe samples to determine amounts of error for the one or moreapproximations of the first segment, compare the amounts of error forthe one or more approximations of the first segment with a first errorthreshold, wherein an order of the first segment order approximation isdetermined based on the comparison of the amounts of error for the oneor more approximations of the first segment with the first errorthreshold, generate one or more approximations of the second segment,compare one or more values of the one or more approximations of thesecond segment with values of the second portion of the samples todetermine amounts of error for the one or more approximations of thesecond segment, and compare the amounts of error for the one or moreapproximations of the second segment with a second error threshold,wherein an order of the second segment order approximation is determinedbased on the comparison of the amounts of error for the one or moreapproximations of the second segment with the second error threshold.

Example 8 may include the device of example 1, wherein to perform thedecimation of the first segment includes to produce a first decimationmap for the first segment based on the first segment orderapproximation, wherein the first decimation map is utilized for thedecimation of the first segment, and to perform the decimation of thesecond segment includes to produce a second decimation map for thesecond segment based on the second segment order approximation, whereinthe second decimation map is utilized for the decimation of the secondsegment.

Example 9 may include the device of example 8, wherein themicrocontroller is further to provide a result of the decimation of thefirst segment to a remote device, provide the first decimation map tothe remote device, provide a result of the decimation of the secondsegment to the remote device, and provide the second decimation map tothe remote device, wherein the remote device is to utilize the firstdecimation map with the first segment and the second decimation map withthe second segment to produce a representation of the signal.

Example 10 may include one or more computer-readable media havinginstructions stored thereon, wherein the instructions, when executed bya device, cause the device to identify a first portion of samples of asignal, the first portion of the samples identified based on a firstsegmentation feature common to the first portion of the samples,identify a second portion of the samples, the second portion of thesamples identified based on a second segmentation feature common to thesecond portion of the samples, determine a first decimation map for thefirst portion of the samples based on an approximation of the firstportion of the samples and a first error threshold for the first portionof the samples, and determine a second decimation map for the secondportion of the samples based on an approximation of the second portionof the samples and a second error threshold for the second portion ofthe samples.

Example 11 may include the one or more computer-readable media ofexample 10, wherein to determine the first decimation map includes togenerate one or more approximations of the first portion of the samples,wherein each of the one or more approximations of the first portion isof different order, and compare values of the one or more approximationsof the first portion of the samples with the first portion of thesamples to determine which of the one or more approximations of thefirst portion of the samples are within the first error threshold,wherein the approximation of the first portion of the samples utilizedfor determination of the first decimation map is included in the one ormore approximations of the first portion of the samples that are withinthe first error threshold, and to determine the second decimation mapincludes to generate one or more approximations of the second portion ofthe samples, wherein each of the one or more approximations of thesecond portion is of different order, and compare values of the one ormore approximations of the second portion of the samples with the secondportion of the samples to determine which of the one or moreapproximations of the second portion of the samples are within thesecond error threshold, wherein the approximation of the second portionof the samples utilized for determination of the second decimation mapis included in the one or more approximations of the second portion ofthe samples that are within the second error threshold.

Example 12 may include the one or more computer-readable media ofexample 10, wherein the first segmentation feature comprises the firstportion of the samples exceeding a magnitude, and wherein the secondsegmentation feature comprises the second portion of the samples beingbelow the magnitude.

Example 13 may include the one or more computer-readable media ofexample 10, wherein the instructions, when executed by the device,further cause the device to perform a first decimation procedure to thefirst portion of the samples with the first decimation map, and performa second decimation procedure to the second portion of the samples withthe second decimation map.

Example 14 may include the one or more computer-readable media ofexample 13, wherein the instructions, when executed by the device,further cause the device to provide a result of the first decimationprocedure to a remote device, provide a result of the second decimationprocedure to the remote device, provide the first decimation map to theremote device, and provide the second decimation map to the remotedevice, the remote device to utilize the first decimation map with theresult of the first decimation procedure and utilize the seconddecimation map with the result of the second decimation procedure toproduce a representation of the signal.

Example 15 may include the one or more computer-readable media ofexample 13, wherein the instructions, when executed by the device,further cause the device to apply delta coding to a result of the firstdecimation procedure, and apply delta coding to a result of the seconddecimation procedure.

Example 16 may include the one or more computer-readable media ofexample 13, wherein the instructions, when executed by the device,further cause the device to apply Huffman coding to a result of thefirst decimation procedure, and apply Huffman coding to a result of thesecond decimation procedure.

Example 17 may include a method for decimating samples of a signal,comprising identifying a first portion of the samples having a firstsegmentation feature, identifying a second portion of the samples havinga second segmentation feature, determining a first order for decimationof the first portion of the samples based on one or more approximationsof the first portion of the samples and a first error threshold for thefirst portion of the samples, and determining a second order fordecimation of the second portion of the samples based on one or moreapproximations of the second portion of the samples and a second errorthreshold for the second portion of the samples.

Example 18 may include the method of example 17, wherein the first orderindicates a first number of samples to be averaged for the decimation ofthe first portion of the samples, and wherein the second order indicatesa second number of samples to be averaged for the decimation of thesecond portion of the samples.

Example 19 may include the method of example 17, further comprisinggenerating a first decimation map for the first portion of the samplesbased on the first order, and generating a second decimation map for thesecond portion of the samples based on the second order.

Example 20 may include the method of example 19, further comprisingdecimating the first portion of the samples via the first decimationmap, wherein a result of the decimating of the first portion of thesamples and the first decimation map is to be utilized for producing afirst portion of a representation of the signal, and decimating thesecond portion of the samples via the second decimation map, wherein aresult of the decimating of the second portion of the samples and thesecond decimation map is to be utilized for producing a second portionof the representation of the signal.

Example 21 may include a device, comprising a memory device to store alead model and a lead measurement, a microcontroller coupled to thememory device, the microcontroller to generate a lead estimate based onthe lead model and the lead measurement, determine a difference betweenthe lead estimate and the lead measurement to produce a representationof the difference, and compress the representation of the difference toproduce a lead error.

Example 22 may include the device of example 21, wherein to generate thelead estimate includes to find peaks within the lead measurement,generate a timing reference based on the peaks, and perform areconstruction with the lead model and timing reference to produce thelead estimate.

Example 23 may include the device of example 21, wherein the leadmeasurement comprises a first lead measurement, and wherein themicrocontroller is further to identify one or more training sequencesfrom at least a second lead measurement, find one or more peaks withinthe one or more training sequences, generate a timing reference based onthe one or more training sequences and the one or more peaks, andconstruct the lead model based on the timing reference and the at leastthe second lead measurement.

Example 24 may include the device of example 21, wherein to compress therepresentation of the difference to produce the lead error comprisesincludes to determine a decimation map for the representation of thedifference, and decimate the representation of the difference accordingto the decimation map to produce the lead error.

The foregoing outlines features of one or more embodiments of thesubject matter disclosed herein. These embodiments are provided toenable a person having ordinary skill in the art (PHOSITA) to betterunderstand various aspects of the present disclosure. Certainwell-understood terms, as well as underlying technologies and/orstandards may be referenced without being described in detail. It isanticipated that the PHOSITA will possess or have access to backgroundknowledge or information in those technologies and standards sufficientto practice the teachings of the present disclosure.

The PHOSITA will appreciate that they may readily use the presentdisclosure as a basis for designing or modifying other processes,structures, or variations for carrying out the same purposes and/orachieving the same advantages of the embodiments introduced herein. ThePHOSITA will also recognize that such equivalent constructions do notdepart from the spirit and scope of the present disclosure, and thatthey may make various changes, substitutions, and alterations hereinwithout departing from the spirit and scope of the present disclosure.

The foregoing outlines features of several embodiments so that thoseskilled in the art may better understand the aspects of the presentdisclosure. Those skilled in the art should appreciate that they mayreadily use the present disclosure as a basis for designing or modifyingother processes and structures for carrying out the same purposes and/orachieving the same advantages of the embodiments introduced herein.Those skilled in the art should also realize that such equivalentconstructions do not depart from the spirit and scope of the presentdisclosure, and that they may make various changes, substitutions, andalterations herein without departing from the spirit and scope of thepresent disclosure.

The particular embodiments of the present disclosure may readily includea system on chip (SoC) central processing unit (CPU) package. An SoCrepresents an integrated circuit (IC) that integrates components of acomputer or other electronic system into a single chip. It may containdigital, analog, mixed-signal, and radio frequency functions: all ofwhich may be provided on a single chip substrate. Other embodiments mayinclude a multi-chip-module (MCM), with a plurality of chips locatedwithin a single electronic package and configured to interact closelywith each other through the electronic package. Any module, function, orblock element of an ASIC or SoC can be provided, where appropriate, in areusable “black box” intellectual property (IP) block, which can bedistributed separately without disclosing the logical details of the IPblock. In various other embodiments, the digital signal processingfunctionalities may be implemented in one or more silicon cores inapplication-specific integrated circuits (ASICs), field-programmablegate arrays (FPGAs), and other semiconductor chips.

In some cases, the teachings of the present disclosure may be encodedinto one or more tangible, non-transitory computer-readable mediumshaving stored thereon executable instructions that, when executed,instruct a programmable device (such as a processor or DSP) to performthe methods or functions disclosed herein. In cases where the teachingsherein are embodied at least partly in a hardware device (such as anASIC, IP block, or SoC), a non-transitory medium could include ahardware device hardware-programmed with logic to perform the methods orfunctions disclosed herein. The teachings could also be practiced in theform of Register Transfer Level (RTL) or other hardware descriptionlanguage such as VHDL or Verilog, which can be used to program afabrication process to produce the hardware elements disclosed.

In example implementations, at least some portions of the processingactivities outlined herein may also be implemented in software. In someembodiments, one or more of these features may be implemented inhardware provided external to the elements of the disclosed figures, orconsolidated in any appropriate manner to achieve the intendedfunctionality. The various components may include software (orreciprocating software) that can coordinate in order to achieve theoperations as outlined herein. In still other embodiments, theseelements may include any suitable algorithms, hardware, software,components, modules, interfaces, or objects that facilitate theoperations thereof.

Additionally, some of the components associated with describedmicroprocessors may be removed, or otherwise consolidated. In a generalsense, the arrangements depicted in the figures may be more logical intheir representations, whereas a physical architecture may includevarious permutations, combinations, and/or hybrids of these elements. Itis imperative to note that countless possible design configurations canbe used to achieve the operational objectives outlined herein.Accordingly, the associated infrastructure has a myriad of substitutearrangements, design choices, device possibilities, hardwareconfigurations, software implementations, equipment options, etc.

Any suitably-configured processor component can execute any type ofinstructions associated with the data to achieve the operations detailedherein. Any processor disclosed herein could transform an element or anarticle (for example, data) from one state or thing to another state orthing. In another example, some activities outlined herein may beimplemented with fixed logic or programmable logic (for example,software and/or computer instructions executed by a processor) and theelements identified herein could be some type of a programmableprocessor, programmable digital logic (for example, an FPGA, an erasableprogrammable read only memory (EPROM), an electrically erasableprogrammable read only memory (EEPROM)), an ASIC that includes digitallogic, software, code, electronic instructions, flash memory, opticaldisks, CD-ROMs, DVD ROMs, magnetic or optical cards, other types ofmachine-readable mediums suitable for storing electronic instructions,or any suitable combination thereof. In operation, processors may storeinformation in any suitable type of non-transitory storage medium (forexample, random access memory (RAM), read only memory (ROM), FPGA,EPROM, electrically erasable programmable ROM (EEPROM), etc.), software,hardware, or in any other suitable component, device, element, or objectwhere appropriate and based on particular needs. Further, theinformation being tracked, sent, received, or stored in a processorcould be provided in any database, register, table, cache, queue,control list, or storage structure, based on particular needs andimplementations, all of which could be referenced in any suitabletimeframe. Any of the memory items discussed herein should be construedas being encompassed within the broad term ‘memory.’ Similarly, any ofthe potential processing elements, modules, and machines describedherein should be construed as being encompassed within the broad term‘microprocessor’ or ‘processor.’ Furthermore, in various embodiments,the processors, memories, network cards, buses, storage devices, relatedperipherals, and other hardware elements described herein may berealized by a processor, memory, and other related devices configured bysoftware or firmware to emulate or virtualize the functions of thosehardware elements.

Computer program logic implementing all or part of the functionalitydescribed herein is embodied in various forms, including, but in no waylimited to, a source code form, a computer executable form, a hardwaredescription form, and various intermediate forms (for example, maskworks, or forms generated by an assembler, compiler, linker, orlocator). In an example, source code includes a series of computerprogram instructions implemented in various programming languages, suchas an object code, an assembly language, or a high-level language suchas OpenCL, RTL, Verilog, VHDL, Fortran, C, C++, JAVA, or HTML for usewith various operating systems or operating environments. The sourcecode may define and use various data structures and communicationmessages. The source code may be in a computer executable form (e.g.,via an interpreter), or the source code may be converted (e.g., via atranslator, assembler, or compiler) into a computer executable form.

In one example embodiment, any number of electrical circuits of theFIGURES may be implemented on a board of an associated electronicdevice. The board can be a general circuit board that can hold variouscomponents of the internal electronic system of the electronic deviceand, further, provide connectors for other peripherals. Morespecifically, the board can provide the electrical connections by whichthe other components of the system can communicate electrically. Anysuitable processors (inclusive of digital signal processors,microprocessors, supporting chipsets, etc.), memory elements, etc. canbe suitably coupled to the board based on particular configurationneeds, processing demands, computer designs, etc. Other components suchas external storage, additional sensors, controllers for audio/videodisplay, and peripheral devices may be attached to the board as plug-incards, via cables, or integrated into the board itself. In anotherexample embodiment, the electrical circuits of the FIGURES may beimplemented as standalone modules (e.g., a device with associatedcomponents and circuitry configured to perform a specific application orfunction) or implemented as plug-in modules into application-specifichardware of electronic devices.

Note that with the numerous examples provided herein, interaction may bedescribed in terms of two, three, four, or more electrical components.However, this has been done for purposes of clarity and example only. Itshould be appreciated that the system can be consolidated in anysuitable manner. Along similar design alternatives, any of theillustrated components, modules, and elements of the FIGURES may becombined in various possible configurations, all of which are clearlywithin the broad scope of this disclosure. In certain cases, it may beeasier to describe one or more of the functionalities of a given set offlows by only referencing a limited number of electrical elements. Itshould be appreciated that the electrical circuits of the FIGURES andits teachings are readily scalable and can accommodate a large number ofcomponents, as well as more complicated/sophisticated arrangements andconfigurations. Accordingly, the examples provided should not limit thescope or inhibit the broad teachings of the electrical circuits aspotentially applied to a myriad of other architectures.

Numerous other changes, substitutions, variations, alterations, andmodifications may be ascertained to one skilled in the art and it isintended that the present disclosure encompass all such changes,substitutions, variations, alterations, and modifications as fallingwithin the scope of the appended claims. In order to assist the UnitedStates Patent and Trademark Office (USPTO) and, additionally, anyreaders of any patent issued on this application in interpreting theclaims appended hereto, Applicant wishes to note that the Applicant: (a)does not intend any of the appended claims to invoke 35 U.S.C. § 112(f)as it exists on the date of the filing hereof unless the words “meansfor” or “steps for” are specifically used in the particular claims; and(b) does not intend, by any statement in the disclosure, to limit thisdisclosure in any way that is not otherwise reflected in the appendedclaims.

What is claimed is:
 1. A device, comprising: a memory device to storesamples of a signal; and a microcontroller coupled to the memory device,the microcontroller to: assign a first portion of the samples to a firstsegment based on a segmentation feature of the first portion of thesamples; assign a second portion of the samples to a second segmentbased on a segmentation feature of the second portion of the samples;perform decimation of the first segment, wherein the decimation of thefirst segment utilizes a first segment order approximation for thedecimation of the first segment; and perform decimation of the secondsegment, wherein the decimation of the second segment utilizes a secondsegment order approximation for the decimation of the second segment. 2.The device of claim 1, wherein the segmentation feature of the firstportion of the samples comprises values of the first portion of thesamples exceeding a certain magnitude, and wherein the segmentationfeature of the second portion of the samples comprises values of thesecond portion of the samples being below the certain magnitude.
 3. Thedevice of claim 1, wherein the first segment order approximation isdetermined based on an average of the first portion of the samples and afirst error threshold for the first segment, and wherein the secondsegment order approximation is determined based on an average of thesecond portion of the samples and a second error threshold for thesecond segment.
 4. The device of claim 3, wherein the first errorthreshold is determined based on the segmentation feature of the firstportion of the samples, and wherein the second error threshold isdetermined based on the segmentation feature of the second portion ofthe samples.
 5. The device of claim 4, wherein the first error thresholdand the second error threshold are different.
 6. The device of claim 1,wherein the first segment order approximation is of a different orderfrom the second segment order approximation.
 7. The device of claim 1,wherein the microcontroller is further to: generate one or moreapproximations of the first segment; compare one or more values of theone or more approximations of the first segment with values of the firstportion of the samples to determine amounts of error for the one or moreapproximations of the first segment; compare the amounts of error forthe one or more approximations of the first segment with a first errorthreshold, wherein an order of the first segment order approximation isdetermined based on the comparison of the amounts of error for the oneor more approximations of the first segment with the first errorthreshold; generate one or more approximations of the second segment;compare one or more values of the one or more approximations of thesecond segment with values of the second portion of the samples todetermine amounts of error for the one or more approximations of thesecond segment; and compare the amounts of error for the one or moreapproximations of the second segment with a second error threshold,wherein an order of the second segment order approximation is determinedbased on the comparison of the amounts of error for the one or moreapproximations of the second segment with the second error threshold. 8.The device of claim 1, wherein: to perform the decimation of the firstsegment includes to produce a first decimation map for the first segmentbased on the first segment order approximation, wherein the firstdecimation map is utilized for the decimation of the first segment; andto perform the decimation of the second segment includes to produce asecond decimation map for the second segment based on the second segmentorder approximation, wherein the second decimation map is utilized forthe decimation of the second segment.
 9. The device of claim 8, whereinthe microcontroller is further to: provide a result of the decimation ofthe first segment to a remote device; provide the first decimation mapto the remote device; provide a result of the decimation of the secondsegment to the remote device; and provide the second decimation map tothe remote device, wherein the remote device is to utilize the firstdecimation map with the first segment and the second decimation map withthe second segment to produce a representation of the signal.
 10. One ormore computer-readable media having instructions stored thereon, whereinthe instructions, when executed by a device, cause the device to:identify a first portion of samples of a signal, the first portion ofthe samples identified based on a first segmentation feature common tothe first portion of the samples; identify a second portion of thesamples, the second portion of the samples identified based on a secondsegmentation feature common to the second portion of the samples;determine a first decimation map for the first portion of the samplesbased on an approximation of the first portion of the samples and afirst error threshold for the first portion of the samples; anddetermine a second decimation map for the second portion of the samplesbased on an approximation of the second portion of the samples and asecond error threshold for the second portion of the samples.
 11. Theone or more computer-readable media of claim 10, wherein: to determinethe first decimation map includes to: generate one or moreapproximations of the first portion of the samples, wherein each of theone or more approximations of the first portion is of different order;and compare values of the one or more approximations of the firstportion of the samples with the first portion of the samples todetermine which of the one or more approximations of the first portionof the samples are within the first error threshold, wherein theapproximation of the first portion of the samples utilized fordetermination of the first decimation map is included in the one or moreapproximations of the first portion of the samples that are within thefirst error threshold; and to determine the second decimation mapincludes to: generate one or more approximations of the second portionof the samples, wherein each of the one or more approximations of thesecond portion is of different order; and compare values of the one ormore approximations of the second portion of the samples with the secondportion of the samples to determine which of the one or moreapproximations of the second portion of the samples are within thesecond error threshold, wherein the approximation of the second portionof the samples utilized for determination of the second decimation mapis included in the one or more approximations of the second portion ofthe samples that are within the second error threshold.
 12. The one ormore computer-readable media of claim 10, wherein the first segmentationfeature comprises the first portion of the samples exceeding amagnitude, and wherein the second segmentation feature comprises thesecond portion of the samples being below the magnitude.
 13. The one ormore computer-readable media of claim 10, wherein the instructions, whenexecuted by the device, further cause the device to: perform a firstdecimation procedure to the first portion of the samples with the firstdecimation map; and perform a second decimation procedure to the secondportion of the samples with the second decimation map.
 14. The one ormore computer-readable media of claim 13, wherein the instructions, whenexecuted by the device, further cause the device to: provide a result ofthe first decimation procedure to a remote device; provide a result ofthe second decimation procedure to the remote device; provide the firstdecimation map to the remote device; and provide the second decimationmap to the remote device, the remote device to utilize the firstdecimation map with the result of the first decimation procedure andutilize the second decimation map with the result of the seconddecimation procedure to produce a representation of the signal.
 15. Theone or more computer-readable media of claim 13, wherein theinstructions, when executed by the device, further cause the device to:apply delta coding to a result of the first decimation procedure; andapply delta coding to a result of the second decimation procedure. 16.The one or more computer-readable media of claim 13, wherein theinstructions, when executed by the device, further cause the device to:apply Huffman coding to a result of the first decimation procedure; andapply Huffman coding to a result of the second decimation procedure. 17.A device, comprising: a memory device to store a lead model and a leadmeasurement; a microcontroller coupled to the memory device, themicrocontroller to: generate a lead estimate based on the lead model andthe lead measurement; determine a difference between the lead estimateand the lead measurement to produce a representation of the difference;and compress the representation of the difference to produce a leaderror.
 18. The device of claim 17, wherein to generate the lead estimateincludes to: find peaks within the lead measurement; generate a timingreference based on the peaks; and perform a reconstruction with the leadmodel and timing reference to produce the lead estimate.
 19. The deviceof claim 17, wherein the lead measurement comprises a first leadmeasurement, and wherein the microcontroller is further to: identify oneor more training sequences from at least a second lead measurement; findone or more peaks within the one or more training sequences; generate atiming reference based on the one or more training sequences and the oneor more peaks; and construct the lead model based on the timingreference and the at least the second lead measurement.
 20. The deviceof claim 17, wherein to compress the representation of the difference toproduce the lead error comprises includes to: determine a decimation mapfor the representation of the difference; and decimate therepresentation of the difference according to the decimation map toproduce the lead error.